Imaging and image processing for computer-aided diagnosis and improved patient care are areas of interest to dental practitioners. Among areas of particular interest for computer-aided diagnosis, treatment assessment, and surgery is image segmentation, particularly for tooth regions.
Various approaches have been proposed to address tooth segmentation. For example, Shah et al. in a study entitled “Automatic tooth segmentation using active contour without edges”, 2006, IEEE Biometrics Symposium, describes a method for automating postmortem identification of teeth for deceased individuals based on dental characteristics.
In “Teeth and jaw 3D reconstruction in stomatology”, Proceedings of the International Conference on Medical Information Visualisation—BioMedical Visualisation, pp 23-28, 2007, researchers Krsek et al. describe a method dealing with problems of 3D tissue reconstruction in stomatology. In this process, 3D geometry models of teeth and jaw bones were created based on input CT image data.
Akhoondali et al. proposed a fast automatic method for the segmentation and visualization of teeth in multi-slice CT-scan data of the patient's head in an article entitled “Rapid Automatic Segmentation and Visualization of Teeth in CT-Scan Data”, Journal of Applied Sciences, pp 2031-2044, 2009. The algorithm uses a sequence of processing steps. The mandible and maxilla are separated using maximum intensity projection in the y direction and a step like region separation algorithm. The dental region is separated using maximum intensity projection in the z direction, thresholding and cropping. The teeth are segmented using a region growing algorithm. The results are visualized using iso-surface extraction and surface and volume rendering.
In “Automatic Tooth Region Separation for Dental CT Images”, Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology, pp 897-901, (2008), researchers Gao et al. disclose a method to construct and visualize the individual tooth model from CT image sequences for dental diagnosis and treatment.
Achieving error-free segmentation processing continues to be a challenge. Over-segmentation, with detection of false positives, continues to be a problem and can be troublesome in dental imaging, particularly where teeth are within very close proximity of each other. There is a desire to correctly differentiate foreground from background areas in a volume image.